Stereographic Analysis of Aerial Photography Imagery for Arctic Development and Technology Planning
Bibliographic record
Abstract
Abstract Aerial photography has had many applications since it was first obtained from manned balloons in the US Civil War to map the positions of enemy lines and troop locations. Early applications centered on military use, but with the lowered cost and availability of civilian aircraft after World War II, commercial applications (terrain mapping, urban planning, resource discovery and development, etc) have grown. The application of stereographic techniques to aerial photography has allowed the third dimension, i. e., height, to enter into commercial applications such as terrain mapping, surface mining, logging, urban planning, etc. On an Arctic scientific level, aerial photography has been used to map glaciers, ice edges and ice features. Using stereographic techniques, ice and iceberg volumes and mass could be estimated. Kiakowski, et al, 1982 used this technique to determine iceberg mass off Newfoundland on the eastern Canadian coast as part of the effort to develop iceberg design criteria for the Hibernia structure. Lovas et al, 1993 used similar techniques to estimate iceberg mass in the Barents Sea off northern Norway. In offshore Alaska waters, aerial mapping and stereo-photography techniques were first used by the US Air Force to discover and map ice islands in the 1950s. Later the oil industry employed aerial photography to map the location of sea ice and ice features that could affect offshore exploration and development. Stereographic techniques were used to the estimate the size of ice ridges. Multiyear programs were funded by industry prior to offshore lease sales in the US Beaufort and Chukchi Seas to develop ice design criteria (such as ridge height and width) and to plan for logistical operations (parameters required included number of ridges per mile, percent surface deformation, etc). Shell Oil Company is presently in the process of planning for the development of offshore leases in both the US Beaufort and Chukchi Seas and as part of its activities has incorporated stereographic analysis of aerial photography. Imagery has been obtained for three years already and the analysis results have been used in both EER planning and technology development and logistical considerations for the production phase. This paper will discuss data collection, data analysis and data use. Particular items to be discussed include use of the data in escape craft concept review and selection for EER, rescue philosophy for the escape craft, logistical considerations generated by the data and data analysis resulting from these considerations. Examples of the data and its analysis will be presented to support this discussion. Introduction Shell's objective in Alaska is to find and develop commercial hydrocarbon resources in the Chukchi and Beaufort Outer Continental Shelf. As with all Shell ventures, the company maintains high operational and social performance standards that will bring, with exploration success, economic expansion and new opportunities to communities across Alaska and the Northwest. Since returning to Alaska in 2005, Shell has embarked on an extensive field data acquisition, R&D and technology maturation effort aimed at supporting exploration and future development. This paper focuses re-supply in winter and platform evacuation in case of an emergency, two important considerations for future development, in the safe and reliable operations of an offshore platform in this region.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".